Deep learning models are powerful image classifiers but their opacity hinders their trustworthiness. Explanation methods for capturing the reasoning process within these classifiers faithfully and in a clear manner are scarce, due to their sheer complexity and size. We provide a solution for this problem by defining a novel method for explaining the outputs of image classifiers with debates between two agents, each arguing for a particular class. We obtain these debates as concrete instances of Free Argumentative eXchanges (FAXs), a novel argumentation-based multi-agent framework allowing agents to internalise opinions by other agents differently than originally stated. We define two metrics (consensus and persuasion rate) to assess the usefulness of FAXs as argumentative explanations for image classifiers. We then conduct a number of empirical experiments showing that FAXs perform well along these metrics as well as being more faithful to the image classifiers than conventional, non-argumentative explanation methods. All our implementations can be found at https://github.com/koriavinash1/FAX.

Alison Stribling has learned a lot about infectious disease since she transferred onto Covid-19 response at the health department in Contra Costa County near San Francisco. One of her discoveries: How vital fax machines are to US pandemic response. Across the country, labs and health providers report new Covid-19 cases to local health departments. At Contra Costa Health Services, officials use the data to start contact tracing or send extra help in certain cases, such as at a care home or to an infected health care worker. On a typical day in Contra Costa, only around half of those reports arrive electronically; the rest, as many as hundreds, flow in via the fax line, creating a Sisyphean reading list.

For many people, the term "artificial intelligence" (AI) conjures up mental images of futuristic-looking robots making our lives easier through realistic emotions and behaviors. Sadly, as much as we'd all love to have an animatronic housekeeper, Rosie the Robot isn't rolling through that door any time soon. The reality is that AI is making our lives easier in a variety of ways through a delicately balanced collaboration of human judgment and data-driven science. With the help of AI, our online experiences are more personalized and targeted to our interests than ever before. We no longer have to call a customer service phone line and wait on hold for 15 minutes, since companies are programming chatbots to answer the most frequently asked questions.

When artificial intelligence is used to expedite certain legacy processes, it can act more like a Band-Aid than a cure. Scenarios describing the potential for artificial intelligence (AI) seem to gravitate toward hyperbole. In wonderful scenarios, AI enables nirvanas of instant optimal processes and prescient humanoids. In doomsday scenarios, algorithms go rogue and humans are superfluous, at best, and, at worst, subservient to the new silicon masters. However, both of these scenarios require a sophistication that, at least right now, seems far away.

When we think of breakthroughs in healthcare, we often conjure images of heroic interventions -- the first organ transplantation, robotic surgery, and so on. But in fact many of the greatest leaps in human health have come from far more prosaic interventions -- the safe disposal of human excrement through sewage and sanitation, for example, or handwashing during births and caesarians. We have a similar opportunity in medicine now with the application of artificial intelligence and machine learning. Glamorous projects to do everything from curing cancer to helping paralyzed patients walk through AI have generated enormous expectations. But the greatest opportunity for AI in the near term may come not from headline-grabbing moonshots but from putting computers and algorithms to work on the most mundane drudgery possible.

Mr. Robot is a show built on hacks. The mother of all hacks serves as the big cliffhanger at the end of the show's first season, and nearly every plot development leading up to it was nudged along by some kind of exploit. It's rare to get through an episode without at least one digital intrusion, often drawn from real life. Each week, we'll be running through Mr. Robot's C Y B E R activities -- who got hacked, why, and how much magic would be required to make them actually work. We take one week off on the Mr. Robot Digital After Show (Hacking Robot will be doing a full download tonight on USA; we're back next week) and of course, everything goes off. Stage Two seems to be underway?

(ESAS 2008).

(ICEIS 2008). (ESAS 2008). Fax: 32-26502715 Faculty of Electrical Engineering and Email: ants@iridia.ulb.ac.be Computing, University of Zagreb URL: iridia.ulb.ac.be/ants2008/ Submission requirements may vary for each workshop, but most Multidisciplinary Workshop on Advances in Preference Handling conform to the schedule below.

(ISAIM 2008).

(ISAIM 2008).